Can I query my internal documents directly using just conversational chat?

Yes. If your data is indexed in a Vectara corpus, simply ask your agent: `search the 'employee-handbook' corpus for remote work policies`. The agent uses the `queryTool` to pass your question to Vectara's semantic engine, effortlessly bringing back precisely matching paragraph citations instantly.

How do I remove outdated context files destroying the accuracy of my RAG model?

You don't need to rebuild APIs or use cURL. Tell your AI: `delete document ID 'doc-992a' from my Sales corpus`. It automatically formats the mutation and wipes the poisoned embedding from Vectara's nodes permanently, restoring high accuracy.

Will the RAG Chat tool provide accurate source citations?

Yes. When you instruct the agent to run `execute_rag_chat`, Vectara processes the query against its internal LLM and index, returning a synthesized natural language answer appended solidly with exact document citations, proving the AI isn't hallucinating facts.

Vectara MCP Server for LlamaIndex 7 tools — connect in under 2 minutes

Name: Vectara
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Vectara as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Vectara. "
            "You have 7 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Vectara?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Vectara MCP Server

Connect your Vectara environment to any AI agent to unlock enterprise-grade Retrieval-Augmented Generation (RAG) and semantic search directly inside your conversational IDE or workspace.

LlamaIndex agents combine Vectara tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Semantic Search — Query your indexed private corpora naturally and return highly relevant, grounded documents without traditional keyword matching limitations.
Conversational RAG — Execute fully-fledged interactive chats leveraging Vectara's backend to provide detailed, cited answers strictly based on your secure documents.
Corpus Management — List all available data corpora, retrieve unique keys, and discover the shape of your indexed data environment on the fly.
Document Auditing — Monitor specific document indexes within a corpus, verify correct ingestions, or permanently delete obsolete files avoiding polluted search results.

The Vectara MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Vectara to LlamaIndex via MCP

Follow these steps to integrate the Vectara MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from Vectara

Why Use LlamaIndex with the Vectara MCP Server

LlamaIndex provides unique advantages when paired with Vectara through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Vectara tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Vectara tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Vectara, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Vectara tools were called, what data was returned, and how it influenced the final answer

Vectara + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Vectara MCP Server delivers measurable value.

Hybrid search: combine Vectara real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Vectara to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Vectara for fresh data

Analytical workflows: chain Vectara queries with LlamaIndex's data connectors to build multi-source analytical reports

Vectara MCP Tools for LlamaIndex (7)

These 7 tools become available when you connect Vectara to LlamaIndex via MCP:

delete_corpus_document

This action is irreversible. Permanently removes a document from a corpus

execute_rag_chat

Provide corpus keys and the user query to get a summarized AI response with citations. Executes a RAG-powered chat completion

get_corpus_details

Retrieves metadata and configuration for a specific corpus

list_chat_sessions

Lists previous RAG chat sessions

list_corpora

Lists all corpora (searchable datasets) in the Vectara account

list_corpus_documents

Lists all indexed documents within a specific corpus

perform_semantic_search

Provide one or more comma-separated corpus keys and the query text. Executes a semantic search across one or more corpora

Example Prompts for Vectara in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Vectara immediately.

"List all configured knowledge corpora I have in Vectara."

"Query corpus `cor-81a` for instructions on 'rolling back kubernetes pods' and show only the top 3 best matching results."

"List all active chat context session IDs for the last week."

Troubleshooting Vectara MCP Server with LlamaIndex

Common issues when connecting Vectara to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Vectara + LlamaIndex FAQ

Common questions about integrating Vectara MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Vectara tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.